Quality of Service aware Resource Provisioning in Edge Computing

This position has already been filled.

Host: Vienna University of Technology (TU Wien)Distributed Systems Group


  • Mandatory requirements for all PhD positions
  • Master in Computer Science or equivalent
  • Knowledge of algorithms and data structures
  • Experience with developing large complex software
  • Knowledge in Machine Learning and/or Optimization techniques is a plus
  • Knowledge about Internet of Things and Cloud technologies is a plus
  • Demonstrated ability to write technical reports, articles is a plus


  1. Enable resource provisioning for large-scale Fog environments, considering their volatility.
  2. Develop resource provisioning and management techniques for resource allocation under given Quality of Service (QoS) constraints targeting non-critical applications, ensuring non-interference with critical apps.

Expected Results:

  • Models for Fog resources, and definition of optimization problems and metrics for resource provisioning.
  • Development of prototype provisioning mechanisms for self-adaptation, supporting the fusion and dissolution of groups of Fog Nodes, data/state migration, sharing, replication, as well as the resource allocation services.
  • Development of mechanisms for self-optimizing resource allocation and task scheduling.
  • Evaluate the devised approaches, via simulations and real-world experiments.
  • Develop services that enable pricing plans used in cost-to-serve infrastructure use.

Planned Visits and Collaboration:

  • MDH (Assoc. Prof. Moris Behnam): Define the API for the resource management jointly with MDH and ABB
  • UNIBAP (Dr. Lars Asplund): Demonstrate the runtime resource management using image analysis apps from UNIBAP.
  • ABB (Dr. Magnus Larsson): Evaluate the OPC UA/DDS gateway on the ABB use case.


Within this PhD project, the candidate is expected to develop optimization approaches for resource allocation and task scheduling in the fog. For this, the candidate is expected to apply techniques from the fields of Machine Learning and/or (Linear) Optimization. The work conducted within this project will follow an iterative approach, starting with centralized, optimal solutions but later on working on decentralized, optimal and heuristic approaches.

Relevant publications: